OpenRaiser/Intern-Atlas huggingface dataset:
Machine Learning-driven Autotuning of Graphics Processing Unit Accelerated Computational Fluid Dynamics for Enhanced Performance
Venue: arXiv cs.PF 2024
Year: 2024

Optimizing the performance of computational fluid dynamics (CFD) applications accelerated by graphics processing units (GPUs) is crucial for efficient simulations. In this study, we employed a machine learning-based autotuning technique to optimize 14 key parameters related to GPU kernel scheduling, including the number of thread blocks and threads within a block. Our approach utilizes fully connected neural networks as the underlying machine learning model, with the tuning parameters as inputs to the neural networks and the actual execution time of a simulation as the outputs. To assess the effectiveness of our autotuning approach, we conducted experiments on three different types of GPUs, with computational speeds ranging from low to high. We performed independent training for each GPU model and also explored combined training across multiple GPU models. By leveraging artificial neural networks, our autotuning technique achieved remarkable results in tuning a wide range of parameters, leading to enhanced performance for a CFD code. Importantly, our approach demonstrated its efficacy while requiring only a small fraction of samples from the large parameter search space. This efficiency is attributed to the effectiveness of the fully connected neural networks in capturing the complex relationships between the parameter settings and the resulting performance. Overall, our study showcases the potential of machine learning, specifically fully connected neural networks, in autotuning GPU-accelerated CFD codes. By leveraging this approach, researchers and practitioners can achieve high performance in scientific simulations with optimized parameter configurations.
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
Venue: arXiv cs.CL 2024
Year: 2024

Chain-of-thought prompting (e.g., "Let's think step-by-step") primes large language models to verbalize rationalization for their predictions. While chain-of-thought can lead to dramatic performance gains, benefits appear to emerge only for sufficiently large models (beyond 50B parameters). We show that orders-of-magnitude smaller models (125M -- 1.3B parameters) can still benefit from chain-of-thought prompting. To achieve this, we introduce Symbolic Chain-of-Thought Distillation (SCoTD), a method to train a smaller student model on rationalizations sampled from a significantly larger teacher model. Experiments across several commonsense benchmarks show that: 1) SCoTD enhances the performance of the student model in both supervised and few-shot settings, and especially for challenge sets; 2) sampling many reasoning chains per instance from the teacher is paramount; and 3) after distillation, student chain-of-thoughts are judged by humans as comparable to the teacher, despite orders of magnitude fewer parameters. We test several hypotheses regarding what properties of chain-of-thought samples are important, e.g., diversity vs. teacher likelihood vs. open-endedness. We release our corpus of chain-of-thought samples and code.
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Venue: arXiv cs.LG 2024
Year: 2024

Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable. Currently, most results hinge on unrealistic assumptions about the data distribution -- namely that it comprises a set of i.i.d. trajectories collected by a single logging policy. We consider a more general setting where the dataset may have been gathered adaptively. We develop theory for the TMIS Offline Policy Evaluation (OPE) estimator in this generalized setting for tabular MDPs, deriving high-probability, instance-dependent bounds on its estimation error. We also recover minimax-optimal offline learning in the adaptive setting. Finally, we conduct simulations to empirically analyze the behavior of these estimators under adaptive and non-adaptive regimes.
Join Size Bounds using Lp-Norms on Degree Sequences
Venue: arXiv cs.DB 2024
Year: 2024

Estimating the output size of a query is a fundamental yet longstanding problem in database query processing. Traditional cardinality estimators used by database systems can routinely underestimate the true output size by orders of magnitude, which leads to significant system performance penalty. Recently, upper bounds have been proposed that are based on information inequalities and incorporate sizes and max-degrees from input relations, yet they their main benefit is limited to cyclic queries, because they degenerate to rather trivial formulas on acyclic queries. We introduce a significant extension of the upper bounds, by incorporating $\ell_p$-norms of the degree sequences of join attributes. Our bounds are significantly lower than previously known bounds, even when applied to acyclic queries. These bounds are also based on information theory, they come with a matching query evaluation algorithm, are computable in exponential time in the query size, and are provably tight when all degrees are "simple".
Estimating Policy Effects in a Social Network with Independent Set Sampling
Venue: arXiv cs.SI 2024
Year: 2024

Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Venue: arXiv cs.CV 2024
Year: 2024

Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting when fine-tuned on extremely limited data. Existing works have explored subject-driven generation using a reference set containing a few images. However, few prior works explore DDPM-based domain-driven generation, which aims to learn the common features of target domains while maintaining diversity. This paper proposes a novel DomainStudio approach to adapt DDPMs pre-trained on large-scale source datasets to target domains using limited data. It is designed to keep the diversity of subjects provided by source domains and get high-quality and diverse adapted samples in target domains. We propose to keep the relative distances between adapted samples to achieve considerable generation diversity. In addition, we further enhance the learning of high-frequency details for better generation quality. Our approach is compatible with both unconditional and conditional diffusion models. This work makes the first attempt to realize unconditional few-shot image generation with diffusion models, achieving better quality and greater diversity than current state-of-the-art GAN-based approaches. Moreover, this work also significantly relieves overfitting for conditional generation and realizes high-quality domain-driven generation, further expanding the applicable scenarios of modern large-scale text-to-image models.
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Venue: arXiv cs.CV 2024
Year: 2024

Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 architecture and enhance it with the CNN-Swin Transformer (CST), which is a new attempt at CNN-Transformer fusion. We also introduce three other useful modules: Weighted Efficient Layer Aggregation Networks (W-ELAN), Multiscale Channel Split (MCS), and Concatenate Convolutional Layers (CatConv) in our CST-YOLO to improve small-scale object detection precision. Experimental results show that the proposed CST-YOLO achieves 92.7%, 95.6%, and 91.1% mAP@0.5, respectively, on three blood cell datasets, outperforming state-of-the-art object detectors, e.g., RT-DETR, YOLOv5, and YOLOv7. Our code is available at https://github.com/mkang315/CST-YOLO.
MedLSAM: Localize and Segment Anything Model for 3D CT Images
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in foundation models have shown significant potential in medical image analysis. However, there is still a gap in models specifically designed for medical image localization. To address this, we introduce MedLAM, a 3D medical foundation localization model that accurately identifies any anatomical part within the body using only a few template scans. MedLAM employs two self-supervision tasks: unified anatomical mapping (UAM) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. Furthermore, we developed MedLSAM by integrating MedLAM with the Segment Anything Model (SAM). This innovative framework requires extreme point annotations across three directions on several templates to enable MedLAM to locate the target anatomical structure in the image, with SAM performing the segmentation. It significantly reduces the amount of manual annotation required by SAM in 3D medical imaging scenarios. We conducted extensive experiments on two 3D datasets covering 38 distinct organs. Our findings are twofold: 1) MedLAM can directly localize anatomical structures using just a few template scans, achieving performance comparable to fully supervised models; 2) MedLSAM closely matches the performance of SAM and its specialized medical adaptations with manual prompts, while minimizing the need for extensive point annotations across the entire dataset. Moreover, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced segmentation performance. Our code is public at \href{https://github.com/openmedlab/MedLSAM}
A Note On The Natural Range Of Unambiguous-SAT
Venue: arXiv cs.CC 2024
Year: 2024

We discuss the natural range of the Unambiguous-SAT problem with respect to the number of clauses. We prove that for a given Boolean formula in precise conjunctive normal form with n variables, there exist functions f(n) and g(n) such that if the number of clauses is greater than f(n) then the formula does not have a satisfying truth assignment and if the number of clauses is greater than g(n) then the formula either has a unique satisfying truth assignment or no satisfying truth assignment. The interval between functions f(n) and g(n) is the natural range of the Unambiguous-SAT problem. We also provide several counting rules and an algorithm that determine the unsatisfiability of some formulas in polynomial time.
Constraining Generative Models for Engineering Design with Negative Data
Venue: arXiv cs.LG 2024
Year: 2024

Generative models have recently achieved remarkable success and widespread adoption in society, yet they often struggle to generate realistic and accurate outputs. This challenge extends beyond language and vision into fields like engineering design, where safety-critical engineering standards and non-negotiable physical laws tightly constrain what outputs are considered acceptable. In this work, we introduce a novel training method to guide a generative model toward constraint-satisfying outputs using `negative data' -- examples of what to avoid. Our negative-data generative model (NDGM) formulation easily outperforms classic models, generating 1/6 as many constraint-violating samples using 1/8 as much data in certain problems. It also consistently outperforms other baselines, achieving a balance between constraint satisfaction and distributional similarity that is unsurpassed by any other model in 12 of the 14 problems tested. This widespread superiority is rigorously demonstrated across numerous synthetic tests and real engineering problems, such as ship hull synthesis with hydrodynamic constraints and vehicle design with impact safety constraints. Our benchmarks showcase both the best-in-class performance of our new NDGM formulation and the overall dominance of NDGMs versus classic generative models. We publicly release the code and benchmarks at https://github.com/Lyleregenwetter/NDGMs.
MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation
Venue: arXiv cs.CL 2024
Year: 2024

Humans talk in daily conversations while aligning and negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, we propose MindDial, a novel conversational framework that can generate situated free-form responses with theory-of-mind modeling. We introduce an explicit mind module that can track the speaker's belief and the speaker's prediction of the listener's belief. Then the next response is generated to resolve the belief difference and take task-related action. Our framework is applied to both prompting and fine-tuning-based models, and is evaluated across scenarios involving both common ground alignment and negotiation. Experiments show that models with mind modeling can achieve higher task outcomes when aligning and negotiating common ground. The ablation study further validates the three-level belief design can aggregate information and improve task outcomes in both cooperative and negotiating settings.
Are aligned neural networks adversarially aligned?
Venue: arXiv cs.CL 2024
Year: 2024

Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs. However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated. We show these models can be easily attacked, i.e., induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image. We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models.
Goal-oriented Semantic Communications for Avatar-centric Augmented Reality
Venue: arXiv cs.IT 2024
Year: 2024

Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, hindering its development. Thus, a critical revolution in the Sixth Generation (6G) networks is envisioned through the joint exploitation of information context and its importance to the task, leading to a communication paradigm shift towards semantic and effectiveness levels. However, current research has not yet proposed any explicit and systematic communication framework for AR applications that incorporate these two levels. To fill this research gap, this paper presents a task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness in 6G. Specifically, we first analyse the traditional wireless AR point cloud communication framework and then summarize our proposed semantic information along with the end-to-end wireless communication. We then detail the design blocks of the TSAR framework, covering both semantic and effectiveness levels. Finally, numerous experiments have been conducted to demonstrate that, compared to the traditional point cloud communication framework, our proposed TSAR significantly reduces wireless AR application transmission latency by 95.6%, while improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.
Asynchronous Algorithmic Alignment with Cocycles
Venue: arXiv cs.LG 2024
Year: 2024

State-of-the-art neural algorithmic reasoners make use of message passing in graph neural networks (GNNs). But typical GNNs blur the distinction between the definition and invocation of the message function, forcing a node to send messages to its neighbours at every layer, synchronously. When applying GNNs to learn to execute dynamic programming algorithms, however, on most steps only a handful of the nodes would have meaningful updates to send. One, hence, runs the risk of inefficiencies by sending too much irrelevant data across the graph. But more importantly, many intermediate GNN steps have to learn the identity functions, which is a non-trivial learning problem. In this work, we explicitly separate the concepts of node state update and message function invocation. With this separation, we obtain a mathematical formulation that allows us to reason about asynchronous computation in both algorithms and neural networks. Our analysis yields several practical implementations of synchronous scalable GNN layers that are provably invariant under various forms of asynchrony.
Differentially Private Distributed Estimation and Learning
Venue: arXiv cs.LG 2024
Year: 2024

We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks. Our novel algorithms extend the existing distributed estimation literature and enable the participating agents to estimate a complete sufficient statistic from private signals acquired offline or online over time and to preserve the privacy of their signals and network neighborhoods. This is achieved through linear aggregation schemes with adjusted randomization schemes that add noise to the exchanged estimates subject to differential privacy (DP) constraints, both in an offline and online manner. We provide convergence rate analysis and tight finite-time convergence bounds. We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent's sensitivity to their signal and network characteristics. Our algorithms are amenable to dynamic topologies and balancing privacy and accuracy trade-offs. Finally, to supplement and validate our theoretical results, we run experiments on real-world data from the US Power Grid Network and electric consumption data from German Households to estimate the average power consumption of power stations and households under all privacy regimes and show that our method outperforms existing first-order, privacy-aware, distributed optimization methods.
A Diamond Model Analysis on Twitter's Biggest Hack
Venue: arXiv cs.CR 2024
Year: 2024

Cyberattacks have prominently increased over the past few years now, and have targeted actors from a wide variety of domains. Understanding the motivation, infrastructure, attack vectors, etc. behind such attacks is vital to proactively work against preventing such attacks in the future and also to analyze the economic and social impact of such attacks. In this paper, we leverage the diamond model to perform an intrusion analysis case study of the 2020 Twitter account hijacking Cyberattack. We follow this standardized incident response model to map the adversary, capability, infrastructure, and victim and perform a comprehensive analysis of the attack, and the impact posed by the attack from a Cybersecurity policy standpoint.
Reduce Computational Complexity for Convolutional Layers by Skipping Zeros
Venue: arXiv cs.LG 2024
Year: 2024

Convolutional neural networks necessitate good algorithms to reduce complexity, and sufficient utilization of parallel processors for acceleration. Within convolutional layers, there are three types of operators: convolution used in forward propagation, deconvolution and dilated-convolution utilized in backward propagation. During the execution of these operators, zeros are typically added to tensors, leading to redundant calculations and unnecessary strain on hardware. To circumvent these inefficiencies, we propose the C-K-S algorithm, accompanied by efficient GPU implementations. C-K-S trims filters to exclude zero-padding. For deconvolution and dilated-convolution, C-K-S transforms sparse tensors into dense tensors, and standardizes the local computational rules to simplify the hardware control. The experimental results demonstrate that C-K-S offers good performance in terms of speed and convergence, surpassing the capabilities of PyTorch and cuDNN in certain scenarios.
Structure in Deep Reinforcement Learning: A Survey and Open Problems
Venue: arXiv cs.LG 2024
Year: 2024

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing various real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and improve performance across these crucial metrics, one promising avenue is to incorporate additional structural information about the problem into the RL learning process. Various sub-fields of RL have proposed methods for incorporating such inductive biases. We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure. By leveraging this comprehensive framework, we provide valuable insights into the challenges of structured RL and lay the groundwork for a design pattern perspective on RL research. This novel perspective paves the way for future advancements and aids in developing more effective and efficient RL algorithms that can potentially handle real-world scenarios better.